Industrial Engineer - Modeling & Optimization

Corning is hiring an Industrial Engineer – Modeling & Optimization in Painted Post, NY at Erwin Park Center. This role is primarily onsite, involves approximately 50% travel on average, primarily domestic, with travel levels varying based on project needs and phases, and relocation assistance is available.

This position supports a corporate engineering team that helps Corning sites solve complex operational challenges using mathematical modeling, simulation, optimization, and Python-based tools. While the role is part of a broader engineering group, this person will be the team’s primary resource for this specialized modeling work and will help expand how these tools are used across the organization. The work directly influences decisions tied to capacity, cost, process flow, resource planning, and operational performance across multiple businesses and locations. It is a strong fit for someone who wants both technical depth and the opportunity to help shape a growing capability.

Key Responsibilities

• Develop and apply mathematical programming, simulation, and cost models to solve business and engineering problems
• Analyze process flows, resource allocation, production planning, and operational data to identify improvement opportunities
• Travel to Corning sites to understand processes, gather inputs, and translate real-world operations into model assumptions and recommendations
• Build interactive tools and web-based applications in Python to support analysis, visualization, and decision-making
• Work with cross-functional stakeholders to frame problems, gather requirements, and communicate results to technical and non-technical audiences
• Document modeling approaches, assumptions, and results to support repeatability and knowledge sharing
• Help deploy optimization, simulation, and cost-modeling tools into business use

Minimum Qualifications and Skills

• Bachelor’s degree in Operations Research, Industrial Engineering, Applied Mathematics, or a related quantitative discipline
• Relevant experience applying mathematical modeling, simulation, optimization, and industrial engineering or operations research methods to solve business or engineering problems. Candidates may qualify through a bachelor’s degree with 5+ years of relevant professional experience, a master’s degree with 4+ years of relevant experience, or a PhD with 2+ years of relevant applied experience. Relevant post-doctoral or industrial research experience may also be considered
• Hands-on experience building and using mathematical models and simulation models in a professional or applied research setting
• Experience with optimization/modeling tools such as CPLEX, Gurobi, Pyomo, GLPK, or similar tools
• Experience with simulation/modeling tools such as AnyLogic, FlexSim, SimPy, or similar platforms
• Strong Python capability for data processing, analysis, and application or tool development, including experience with libraries such as Polars, Streamlit, Altair, or similar tools
• Ability to communicate technical concepts clearly to technical and non-technical audiences and work effectively across teams to drive projects forward

Preferred Qualifications and Skills

• Master’s degree preferred; PhD may also be a strong fit
• Experience with data preparation, workflow automation, and data visualization
• Exposure to manufacturing, supply chain, industrial operations, or commercial analytics
• Experience building interactive dashboards, web-based tools, or decision-support applications for end users
• Lean or traditional operational industrial engineering experience is helpful, but not required